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liaoyang_harvest.py
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liaoyang_harvest.py
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import argparse
import os
import pandas as pd
import matplotlib.cm as cm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
import datetime
import seaborn as sns
from utils.common import mkdir, save_curve
from utils.plt_params import plt_fig_params, set_day_xtick
warnings.filterwarnings("ignore")
os.environ['NLS_LANG'] = 'AMERICAN_AMERICA.AL32UTF8'
def Figure5(args):
print("=============Figure5===============")
save_dir = args.base_tmp_folder+'/figure5/'
mkdir(save_dir)
expr_harvest, ctrl_harvest = get_harvest(args)
price = get_price(args)
# save curve
figure5a_experimental = {"xlabel": 'date', "ylabel": "kg/m2",
"x": range(len(expr_harvest['production'])),
"y": expr_harvest['production']}
figure5a_control = {"xlabel": 'date', "ylabel": "kg/m2",
"x": range(len(ctrl_harvest['production'])),
"y": ctrl_harvest['production']}
figure5b_experimental = {"xlabel": 'date', "ylabel": "euro/m2",
"x": range(len(expr_harvest['gains'])),
"y": expr_harvest['gains']}
figure5b_control = {"xlabel": 'date', "ylabel": "euro/m2",
"x": range(len(ctrl_harvest['gains'])),
"y": ctrl_harvest['gains']}
figure5c_experimental = {"xlabel": ['Growing expert', 'iGrow'], "ylabel": "euro",
"x": range(len(price['expr'])),
"y": price['expr']}
figure5c_control = {"xlabel": ['Growing expert', 'iGrow'], "ylabel": "euro",
"x": range(len(price['ctrl'])),
"y": price['ctrl']}
save_curve_dir = save_dir + '/curve/'
mkdir(save_curve_dir)
save_curve(figure5a_experimental, save_curve_dir +
'figure5a_experimental.pkl')
save_curve(figure5a_control, save_curve_dir+'figure5a_control.pkl')
save_curve(figure5b_experimental, save_curve_dir +
'figure5b_experimental.pkl')
save_curve(figure5b_control, save_curve_dir+'figure5b_control.pkl')
save_curve(figure5c_experimental, save_curve_dir +
'figure5c_experimental.pkl')
save_curve(figure5c_control, save_curve_dir+'figure5c_control.pkl')
# show
compare_harvest_plot(expr_harvest, ctrl_harvest, price,
startDate=args.startDate,
endDate=args.endDate,
save_fig_dir=args.save_fig_dir)
def get_harvest(args):
harvest_file = os.path.join(args.base_input_path, args.harvest_files)
with open(harvest_file, 'r') as f:
harvest_file_dir = f.readlines()
harvest_file_dir = harvest_file_dir[0].replace("\n", '')
expr_harvest, ctrl_harvest = harvest_analysis(args=args,
harvest_dir=harvest_file_dir)
return expr_harvest, ctrl_harvest
def compare_harvest_plot(expr_harvest, ctrl_harvest, df,
startDate, endDate,
save_fig_dir):
# fig, axes
mpl.rcParams.update(plt_fig_params)
fig = plt.figure(figsize=(13, 6))
layout = (1, 3)
for c in range(layout[1]):
plt.subplot2grid(layout, (0, c), rowspan=1, colspan=1)
props = {0: {"xlabel": "Date",
"ylabel": "Kg/m$^2$",
},
1: {"xlabel": "Date",
"ylabel": "Euro/m$^2$",
},
2: {"ylabel": "Euro/Kg",
},
}
plt_fig_style = {
'Human expert': dict(linestyle='--', lw=2, color=cm.viridis(0.7), label='the control group'),
'EGA': dict(linestyle='-', lw=2, color=cm.viridis(0.3), label='the experimental group'), }
title = ['(a) Crop yield', '(b) Gains', '(c) Fruit Prices']
key = ['production', 'gains', 'price']
yticks_list = [list(range(0, 20, 5)) + [20],
list(range(0, 10, 3))]
for idx, ax in enumerate(fig.axes):
if idx == 2:
sns.boxplot(data=df, notch=0, linewidth=1.5,
order=list(df.columns), dodge=False, width=0.6,
palette=sns.color_palette("viridis_r", 2),
saturation=0.7,
showmeans=True,
meanline=True,
meanprops={'linestyle': '-',
'color': '#393939',
'linewidth': 3},
)
ax.set_xticklabels(labels=['Planting expert', 'iGrow'])
ax.tick_params(axis='x', labelsize=22)
else:
experiment_avg = np.mean(expr_harvest[key[idx]], axis=1)
experiment_std = np.std(expr_harvest[key[idx]], axis=1)
control_avg = np.mean(ctrl_harvest[key[idx]], axis=1)
control_std = np.std(ctrl_harvest[key[idx]], axis=1)
expr_max_id = np.argmax(experiment_avg)
experiment_avg = experiment_avg[:expr_max_id+1]
experiment_std = experiment_std[:expr_max_id+1]
ctrl_max_id = np.argmax(control_avg)
control_avg = control_avg[:ctrl_max_id+1]
control_std = control_std[:ctrl_max_id+1]
expr_iter = np.arange(len(experiment_avg))
ctrl_iter = np.arange(len(control_avg))
ax.plot(expr_iter, experiment_avg, **plt_fig_style['EGA'])
r1 = list(map(lambda x: x[0] - x[1],
zip(experiment_avg, experiment_std)))
r2 = list(map(lambda x: x[0] + x[1],
zip(experiment_avg, experiment_std)))
ax.fill_between(expr_iter, r1, r2, alpha=0.3,
**plt_fig_style['EGA'])
ax.plot(ctrl_iter, control_avg, **plt_fig_style['Human expert'])
r1 = list(map(lambda x: x[0] - x[1],
zip(control_avg, control_std)))
r2 = list(map(lambda x: x[0] + x[1],
zip(control_avg, control_std)))
ax.fill_between(ctrl_iter, r1, r2, alpha=0.3, **
plt_fig_style['Human expert'])
xticks, xlabels = set_day_xtick(num=4,
var_list=list(control_avg[:]),
startDate=startDate,
endDate=endDate)
ax.set_xticks(ticks=xticks)
ax.set_xticklabels(labels=xlabels)
ax.set_yticks(ticks=yticks_list[idx])
ax.tick_params(axis='x', labelsize=20)
ax.grid(linestyle="--", alpha=0.4)
ax.set_title(title[idx], y=-0.38, fontsize=28)
ax.tick_params(axis='y', labelsize=20)
ax.set(**props[idx])
min_xlim, max_xlim = ax.get_xlim()
min_ylim, max_ylim = ax.get_ylim()
xlim_length = abs(max_xlim - min_xlim)
ylim_length = abs(max_ylim - min_ylim)
aspect = xlim_length / ylim_length
ax.set_aspect(aspect)
ax.xaxis.label.set_size(25)
ax.yaxis.label.set_size(25)
plt.tight_layout()
# legend
ax = fig.axes[1]
handles, labels = ax.get_legend_handles_labels()
plt.legend(handles[:2], labels[:2], bbox_to_anchor=(1.1, -0.33), loc='upper right',
ncol=2, framealpha=0, fancybox=False, fontsize=35)
plt.subplots_adjust(bottom=0.43)
mkdir(save_fig_dir)
plt.savefig(save_fig_dir+'liaoyang2_harvest.png', bbox_inches='tight')
plt.close()
def harvest_analysis(args, harvest_dir):
startDate = datetime.datetime.strptime(args.startDate, "%Y-%m-%d")
endDate = datetime.datetime.strptime(args.endDate, "%Y-%m-%d")
days = (endDate-startDate).days + 1
expr_prod = np.zeros((days, len(args.experiment_gh)))
ctrl_prod = np.zeros((days, len(args.control_group)))
expr_gains = np.zeros((days, len(args.experiment_gh)))
ctrl_gains = np.zeros((days, len(args.control_group)))
m2_to_Mu = 667
production = pd.read_csv(harvest_dir + 'production.csv')
production = production.values[:, 1:] / m2_to_Mu
Income = pd.read_csv(harvest_dir + 'Income.csv')
Income = Income.values[:, 1:] / m2_to_Mu * args.rmb2euro
ctrl_prod[-len(production):, :] = np.nancumsum(production[:, :2], axis=0)
expr_prod[-len(production):, :] = np.nancumsum(production[:, 2:], axis=0)
ctrl_gains[-len(Income):, :] = np.nancumsum(Income[:, :2], axis=0)
expr_gains[-len(Income):, :] = np.nancumsum(Income[:, 2:], axis=0)
expr_harvest = {"production": expr_prod,
"gains": expr_gains}
ctrl_harvest = {"production": ctrl_prod,
"gains": ctrl_gains}
return expr_harvest, ctrl_harvest
def scatter_data(df, col, pos_x):
expr = df[col].values
expr_dic = dict(zip(*np.unique(expr, return_counts=True)))
Y = []
Val = []
for k, v in expr_dic.items():
if k != 'nan':
Y.append(float(k))
Val.append(v)
X = [pos_x] * len(Y)
X = np.array(X)
Y = np.array(Y)
Val = np.array(Val)
return X, Y, Val
def get_price(args):
harvest_file = os.path.join(args.base_input_path, args.harvest_files)
with open(harvest_file, 'r') as f:
harvest_file_dir = f.readlines()
harvest_file_dir = harvest_file_dir[0].replace("\n", '')
harvest_price_dir = os.path.join(harvest_file_dir, 'price.csv')
df = pd.read_csv(harvest_price_dir)
ctrl_price = df.values[:, 1:3]
expr_price = df.values[:, 3:]
expr_price = expr_price.astype(np.float32) * args.rmb2euro
ctrl_price = ctrl_price.astype(np.float32) * args.rmb2euro
expr_price[expr_price == 0] = np.nan
ctrl_price[ctrl_price == 0] = np.nan
expr_price = expr_price.flatten()
ctrl_price = ctrl_price.flatten()
price = np.full((expr_price.shape[0], 2), np.nan)
columns = ['ctrl', 'expr']
price[:ctrl_price.shape[0], 0] = ctrl_price
price[:, 1] = expr_price
df = pd.DataFrame(price, columns=columns)
df = df.applymap("{0:.01f}".format)
return df
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--startDate', default="2020-03-15",
help='start date of planting.')
parser.add_argument('--endDate', default="2020-07-13",
help='end date of planting.')
parser.add_argument('--control_group', type=list, default=[1, 2],
help='ids of all green house.')
parser.add_argument('--experiment_gh', type=list, default=[3, 4, 5, 6, 7],
help='ids of all green house.')
parser.add_argument('--rmb2euro', type=float, default=0.1276,
help="rate of rmb to euro")
parser.add_argument("--base_input_path", default="./input", type=str)
parser.add_argument("--base_tmp_folder", default="./result", type=str)
parser.add_argument("--harvest_files", default='harvest.txt', type=str)
parser.add_argument('--save_fig_dir', type=str, default='result/figure5/',
help="save figures directory")
args = parser.parse_args()
Figure5(args)